{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following is adapted from [Visualizing TensorFlow Graphs in Jupyter Notebooks](https://blog.jakuba.net/2017/05/30/tensorflow-visualization.html)\n",
    "\n",
    "And excuted in\n",
    "\n",
    "```bash\n",
    "docker run -it -p 8888:8888 -p 6006:6006 -v `pwd`:/space/ -w /space/ --rm --name md waleedka/modern-deep-learning jupyter notebook --ip=0.0.0.0 --allow-root\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "g = tf.Graph()\n",
    "\n",
    "with g.as_default():\n",
    "    a = tf.placeholder(tf.float32, name=\"a\")\n",
    "    b = tf.placeholder(tf.float32, name=\"b\")\n",
    "    c = a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'add']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[node.name for node in g.as_graph_def().node]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.as_graph_def().node[2].input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hit:1 http://archive.ubuntu.com/ubuntu xenial InRelease\n",
      "Get:2 http://security.ubuntu.com/ubuntu xenial-security InRelease [102 kB]\n",
      "Get:3 http://archive.ubuntu.com/ubuntu xenial-updates InRelease [102 kB]\n",
      "Get:4 http://archive.ubuntu.com/ubuntu xenial-backports InRelease [102 kB]\n",
      "Fetched 306 kB in 2s (123 kB/s)\n",
      "Reading package lists...\n",
      "Reading package lists...\n",
      "Building dependency tree...\n",
      "Reading state information...\n",
      "graphviz is already the newest version (2.38.0-12ubuntu2.1).\n",
      "0 upgraded, 0 newly installed, 0 to remove and 73 not upgraded.\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "export DEBIAN_FRONTEND=noninteractive\n",
    "apt-get update\n",
    "apt-get install -yq --no-install-recommends graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting graphviz\n",
      "  Downloading graphviz-0.8.1-py2.py3-none-any.whl\n",
      "Installing collected packages: graphviz\n",
      "Successfully installed graphviz-0.8.1\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "pip install graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\n",
       " -->\n",
       "<!-- Title: %3 Pages: 1 -->\n",
       "<svg width=\"134pt\" height=\"116pt\"\n",
       " viewBox=\"0.00 0.00 134.00 116.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 112)\">\n",
       "<title>%3</title>\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-112 130,-112 130,4 -4,4\"/>\n",
       "<!-- a -->\n",
       "<g id=\"node1\" class=\"node\"><title>a</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"27\" cy=\"-90\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"27\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">a</text>\n",
       "</g>\n",
       "<!-- add -->\n",
       "<g id=\"node3\" class=\"node\"><title>add</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"63\" cy=\"-18\" rx=\"27.8951\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"63\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">add</text>\n",
       "</g>\n",
       "<!-- a&#45;&gt;add -->\n",
       "<g id=\"edge1\" class=\"edge\"><title>a&#45;&gt;add</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M35.3496,-72.7646C39.6492,-64.4043 44.9918,-54.0159 49.8312,-44.6059\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"52.9847,-46.127 54.4457,-35.6334 46.7596,-42.9256 52.9847,-46.127\"/>\n",
       "</g>\n",
       "<!-- b -->\n",
       "<g id=\"node2\" class=\"node\"><title>b</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"99\" cy=\"-90\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"99\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">b</text>\n",
       "</g>\n",
       "<!-- b&#45;&gt;add -->\n",
       "<g id=\"edge2\" class=\"edge\"><title>b&#45;&gt;add</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M90.6504,-72.7646C86.3508,-64.4043 81.0082,-54.0159 76.1688,-44.6059\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"79.2404,-42.9256 71.5543,-35.6334 73.0153,-46.127 79.2404,-42.9256\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.dot.Digraph at 0x7fe3406c56a0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from graphviz import Digraph\n",
    "\n",
    "dot = Digraph()\n",
    "\n",
    "for n in g.as_graph_def().node:\n",
    "    # Each node has a name and a label. The name identifies the node\n",
    "    # while the label is what will be displayed in the graph.\n",
    "    # We're using the name as a label for simplicity.\n",
    "    dot.node(n.name, label=n.name)\n",
    "    \n",
    "    for i in n.input:\n",
    "        # Edges are determined by the names of the nodes\n",
    "        dot.edge(i, n.name)\n",
    "        \n",
    "# Jupyter can automatically display the DOT graph,\n",
    "# which allows us to just return it as a value.\n",
    "dot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tf_to_dot(graph):\n",
    "    dot = Digraph()\n",
    "\n",
    "    for n in g.as_graph_def().node:\n",
    "        dot.node(n.name, label=n.name)\n",
    "\n",
    "        for i in n.input:\n",
    "            dot.edge(i, n.name)\n",
    "            \n",
    "    return dot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\n",
       " -->\n",
       "<!-- Title: %3 Pages: 1 -->\n",
       "<svg width=\"134pt\" height=\"188pt\"\n",
       " viewBox=\"0.00 0.00 134.00 188.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 184)\">\n",
       "<title>%3</title>\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-184 130,-184 130,4 -4,4\"/>\n",
       "<!-- pi -->\n",
       "<g id=\"node1\" class=\"node\"><title>pi</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"27\" cy=\"-162\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"27\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">pi</text>\n",
       "</g>\n",
       "<!-- mul -->\n",
       "<g id=\"node3\" class=\"node\"><title>mul</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"42\" cy=\"-90\" rx=\"28.6953\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"42\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">mul</text>\n",
       "</g>\n",
       "<!-- pi&#45;&gt;mul -->\n",
       "<g id=\"edge1\" class=\"edge\"><title>pi&#45;&gt;mul</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M30.6311,-144.055C32.3012,-136.261 34.3239,-126.822 36.1974,-118.079\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"39.6251,-118.787 38.2982,-108.275 32.7805,-117.32 39.6251,-118.787\"/>\n",
       "</g>\n",
       "<!-- r -->\n",
       "<g id=\"node2\" class=\"node\"><title>r</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"99\" cy=\"-162\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"99\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">r</text>\n",
       "</g>\n",
       "<!-- r&#45;&gt;mul -->\n",
       "<g id=\"edge2\" class=\"edge\"><title>r&#45;&gt;mul</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M86.6318,-145.811C79.1911,-136.673 69.563,-124.849 61.155,-114.524\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"63.767,-112.188 54.7387,-106.644 58.339,-116.608 63.767,-112.188\"/>\n",
       "</g>\n",
       "<!-- mul_1 -->\n",
       "<g id=\"node4\" class=\"node\"><title>mul_1</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"70\" cy=\"-18\" rx=\"38.9931\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"70\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">mul_1</text>\n",
       "</g>\n",
       "<!-- r&#45;&gt;mul_1 -->\n",
       "<g id=\"edge4\" class=\"edge\"><title>r&#45;&gt;mul_1</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M95.5019,-143.871C90.5376,-119.564 81.3996,-74.8187 75.5168,-46.0132\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"78.9407,-45.2867 73.5105,-36.1893 72.0823,-46.6874 78.9407,-45.2867\"/>\n",
       "</g>\n",
       "<!-- mul&#45;&gt;mul_1 -->\n",
       "<g id=\"edge3\" class=\"edge\"><title>mul&#45;&gt;mul_1</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M48.6356,-72.411C51.8659,-64.3352 55.8275,-54.4312 59.4581,-45.3547\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"62.7532,-46.5409 63.2175,-35.9562 56.2539,-43.9411 62.7532,-46.5409\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.dot.Digraph at 0x7fe3406c2198>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = tf.Graph()\n",
    "\n",
    "with g.as_default():\n",
    "    pi = tf.constant(3.14, name=\"pi\")\n",
    "    r = tf.placeholder(tf.float32, name=\"r\")\n",
    "    \n",
    "    y = pi * r * r\n",
    "    \n",
    "tf_to_dot(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "mkdir vis_logs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the follwing:\n",
    "\n",
    "```bash\n",
    "docker exec -it md tensorboard --logdir=dl/vis_logs\n",
    "```\n",
    "\n",
    "And navigate to http://localhost:6006/#graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "g = tf.Graph()\n",
    "\n",
    "with g.as_default():\n",
    "    pi = tf.constant(3.14, name=\"pi\")\n",
    "    r = tf.placeholder(tf.float32, name=\"r\")\n",
    "    \n",
    "    y = pi * r * r\n",
    "    \n",
    "tf.summary.FileWriter(\"vis_logs\", g).close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://i.imgur.com/XajS4Tv.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "g = tf.Graph()\n",
    "\n",
    "with g.as_default():\n",
    "    X = tf.placeholder(tf.float32, name=\"X\")\n",
    "    \n",
    "    W1 = tf.placeholder(tf.float32, name=\"W1\")\n",
    "    b1 = tf.placeholder(tf.float32, name=\"b1\")\n",
    "    \n",
    "    a1 = tf.nn.relu(tf.matmul(X, W1) + b1)\n",
    "    \n",
    "    W2 = tf.placeholder(tf.float32, name=\"W2\")\n",
    "    b2 = tf.placeholder(tf.float32, name=\"b2\")\n",
    "    \n",
    "    a2 = tf.nn.relu(tf.matmul(a1, W2) + b2)\n",
    "\n",
    "    W3 = tf.placeholder(tf.float32, name=\"W3\")\n",
    "    b3 = tf.placeholder(tf.float32, name=\"b3\")\n",
    "    \n",
    "    y_hat = tf.matmul(a2, W3) + b3\n",
    "    \n",
    "tf.summary.FileWriter(\"vis_logs\", g).close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://i.imgur.com/pwcEAgb.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "g = tf.Graph()\n",
    "\n",
    "with g.as_default():\n",
    "    X = tf.placeholder(tf.float32, name=\"X\")\n",
    "    \n",
    "    with tf.name_scope(\"Layer1\"):\n",
    "        W1 = tf.placeholder(tf.float32, name=\"W1\")\n",
    "        b1 = tf.placeholder(tf.float32, name=\"b1\")\n",
    "\n",
    "        a1 = tf.nn.relu(tf.matmul(X, W1) + b1)\n",
    "    \n",
    "    with tf.name_scope(\"Layer2\"):\n",
    "        W2 = tf.placeholder(tf.float32, name=\"W2\")\n",
    "        b2 = tf.placeholder(tf.float32, name=\"b2\")\n",
    "\n",
    "        a2 = tf.nn.relu(tf.matmul(a1, W2) + b2)\n",
    "\n",
    "    with tf.name_scope(\"Layer3\"):\n",
    "        W3 = tf.placeholder(tf.float32, name=\"W3\")\n",
    "        b3 = tf.placeholder(tf.float32, name=\"b3\")\n",
    "\n",
    "        y_hat = tf.matmul(a2, W3) + b3\n",
    "    \n",
    "tf.summary.FileWriter(\"vis_logs\", g).close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://i.imgur.com/6HIpspV.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb\n",
    "# TensorFlow Graph visualizer code\n",
    "import numpy as np\n",
    "from IPython.display import clear_output, Image, display, HTML\n",
    "\n",
    "def strip_consts(graph_def, max_const_size=32):\n",
    "    \"\"\"Strip large constant values from graph_def.\"\"\"\n",
    "    strip_def = tf.GraphDef()\n",
    "    for n0 in graph_def.node:\n",
    "        n = strip_def.node.add() \n",
    "        n.MergeFrom(n0)\n",
    "        if n.op == 'Const':\n",
    "            tensor = n.attr['value'].tensor\n",
    "            size = len(tensor.tensor_content)\n",
    "            if size > max_const_size:\n",
    "                tensor.tensor_content = \"<stripped %d bytes>\"%size\n",
    "    return strip_def\n",
    "\n",
    "def show_graph(graph_def, max_const_size=32):\n",
    "    \"\"\"Visualize TensorFlow graph.\"\"\"\n",
    "    if hasattr(graph_def, 'as_graph_def'):\n",
    "        graph_def = graph_def.as_graph_def()\n",
    "    strip_def = strip_consts(graph_def, max_const_size=max_const_size)\n",
    "    code = \"\"\"\n",
    "        <script src=\"//cdnjs.cloudflare.com/ajax/libs/polymer/0.3.3/platform.js\"></script>\n",
    "        <script>\n",
    "          function load() {{\n",
    "            document.getElementById(\"{id}\").pbtxt = {data};\n",
    "          }}\n",
    "        </script>\n",
    "        <link rel=\"import\" href=\"https://tensorboard.appspot.com/tf-graph-basic.build.html\" onload=load()>\n",
    "        <div style=\"height:600px\">\n",
    "          <tf-graph-basic id=\"{id}\"></tf-graph-basic>\n",
    "        </div>\n",
    "    \"\"\".format(data=repr(str(strip_def)), id='graph'+str(np.random.rand()))\n",
    "\n",
    "    iframe = \"\"\"\n",
    "        <iframe seamless style=\"width:1200px;height:620px;border:0\" srcdoc=\"{}\"></iframe>\n",
    "    \"\"\".format(code.replace('\"', '&quot;'))\n",
    "    display(HTML(iframe))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe seamless style=\"width:1200px;height:620px;border:0\" srcdoc=\"\n",
       "        <script src=&quot;//cdnjs.cloudflare.com/ajax/libs/polymer/0.3.3/platform.js&quot;></script>\n",
       "        <script>\n",
       "          function load() {\n",
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   ],
   "source": [
    "# Simply call this to display the result. Unfortunately it doesn't save the output together with\n",
    "# the Jupyter notebook, so we can only show a non-interactive image here.\n",
    "show_graph(g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following is adapted from [Visualizing CNN architectures side by side with mxnet](http://josephpcohen.com/w/visualizing-cnn-architectures-side-by-side-with-mxnet/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting mxnet\n",
      "  Downloading mxnet-0.12.1-py2.py3-none-manylinux1_x86_64.whl (25.7MB)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.5/dist-packages (from mxnet)\n",
      "Requirement already satisfied: graphviz in /usr/local/lib/python3.5/dist-packages (from mxnet)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.5/dist-packages (from mxnet)\n",
      "Requirement already satisfied: idna<2.7,>=2.5 in /usr/local/lib/python3.5/dist-packages (from requests->mxnet)\n",
      "Requirement already satisfied: urllib3<1.23,>=1.21.1 in /usr/local/lib/python3.5/dist-packages (from requests->mxnet)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.5/dist-packages (from requests->mxnet)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.5/dist-packages (from requests->mxnet)\n",
      "Installing collected packages: mxnet\n",
      "Successfully installed mxnet-0.12.1\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "pip install mxnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--2017-11-24 00:26:22--  http://data.dmlc.ml/mxnet/models/imagenet/vgg/vgg19.tar.gz\n",
      "Resolving data.dmlc.ml (data.dmlc.ml)... 54.208.175.7\n",
      "Connecting to data.dmlc.ml (data.dmlc.ml)|54.208.175.7|:80... connected.\n",
      "HTTP request sent, awaiting response... 404 Not Found\n",
      "2017-11-24 00:26:25 ERROR 404: Not Found.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "# https://github.com/dmlc/mxnet-model-gallery/blob/master/imagenet-1k-vgg.md\n",
    "wget http://data.dmlc.ml/mxnet/models/imagenet/vgg/vgg19.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--2017-11-23 01:55:04--  http://data.dmlc.ml/models/imagenet/inception-bn/Inception-BN-symbol.json\n",
      "Resolving data.dmlc.ml (data.dmlc.ml)... 54.208.175.7\n",
      "Connecting to data.dmlc.ml (data.dmlc.ml)|54.208.175.7|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 116922 (114K) [application/json]\n",
      "Saving to: 'Inception-BN-symbol.json'\n",
      "\n",
      "     0K .......... .......... .......... .......... .......... 43% 23.1K 3s\n",
      "    50K .......... .......... .......... .......... .......... 87% 16.4K 1s\n",
      "   100K .......... ....                                       100% 10.2K=6.6s\n",
      "\n",
      "2017-11-23 01:55:13 (17.3 KB/s) - 'Inception-BN-symbol.json' saved [116922/116922]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "wget http://data.dmlc.ml/models/imagenet/inception-bn/Inception-BN-symbol.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
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      "      \"name\": \"data\", \n",
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      "      \"backward_source_id\": -1\n",
      "    }, \n",
      "    {\n",
      "      \"op\": \"null\", \n",
      "      \"param\": {}, \n",
      "      \"name\": \"conv_1_weight\", \n",
      "      \"inputs\": [], \n",
      "      \"backward_source_id\": -1\n",
      "    }, \n",
      "    {\n",
      "      \"op\": \"null\", \n",
      "      \"param\": {}, \n",
      "      \"name\": \"conv_1_bias\", \n",
      "      \"inputs\": [], \n",
      "      \"backward_source_id\": -1\n",
      "    }, \n",
      "    {\n",
      "      \"op\": \"Convolution\", \n",
      "      \"param\": {\n",
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      "        \"cudnn_tune\": \"off\", \n",
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      "      \"op\": \"Convolution\", \n",
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   ],
   "source": [
    "%%bash\n",
    "cat Inception-BN-symbol.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Process is interrupted.\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "wget http://data.dmlc.ml/mxnet/models/imagenet/resnet/50-layers/resnet-50-symbol.json && wget http://data.dmlc.ml/mxnet/models/imagenet/resnet/50-layers/resnet-50-0000.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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     "execution_count": 62,
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   ],
   "source": [
    "import mxnet as mx\n",
    "\n",
    "sym, arg_params, aux_params = mx.model.load_checkpoint('resnet-50', 0)\n",
    "\n",
    "mx.viz.plot_network(sym, node_attrs={\"shape\":'rect',\"fixedsize\":'false'}, save_format='png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
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   ],
   "source": [
    "import mxnet as mx\n",
    "user = mx.symbol.Variable('user')\n",
    "item = mx.symbol.Variable('item')\n",
    "score = mx.symbol.Variable('score')\n",
    "\n",
    "# Set dummy dimensions\n",
    "k = 64\n",
    "max_user = 100\n",
    "max_item = 50\n",
    "\n",
    "# user feature lookup\n",
    "user = mx.symbol.Embedding(data = user, input_dim = max_user, output_dim = k)\n",
    "\n",
    "# item feature lookup\n",
    "item = mx.symbol.Embedding(data = item, input_dim = max_item, output_dim = k)\n",
    "\n",
    "# predict by the inner product, which is elementwise product and then sum\n",
    "net = user * item\n",
    "net = mx.symbol.sum_axis(data = net, axis = 1)\n",
    "net = mx.symbol.Flatten(data = net)\n",
    "\n",
    "# loss layer\n",
    "net = mx.symbol.LinearRegressionOutput(data = net, label = score)\n",
    "\n",
    "# Visualize your network\n",
    "mx.viz.plot_network(net)"
   ]
  },
  {
   "cell_type": "code",
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    "collapsed": true
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   "outputs": [],
   "source": []
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